System and method for identifying wireless coverage for multi-occupant structures

ABSTRACT

A method, a device, and a non-transitory storage medium provide for receive line-of-site (LoS) information obtained by a camera, wherein the LoS information identifies an LoS from a radio site to a potential radio signal receiver location; estimate a signal strength value, for a signal from the radio site, at the potential radio signal receiver location; identify a first type of obstruction in the LoS, wherein a signal loss associated with the first type of obstruction varies by a time of year; modify the estimated signal strength value using a nominal signal loss value for the first type of obstruction, wherein the nominal signal loss value is associated with a different time of year than the time of year in which the LoS information is obtained; and determine whether to qualify the potential radio signal receiver location for an equipment installation for the wireless service based on a threshold signal value.

RELATED APPLICATION

The present application is a continuation of, and claims priority from,U.S. application Ser. No. 16/156,142 filed Oct. 10, 2018, entitled“System and Method for Identifying Wireless Coverage for Multi-OccupantStructures,” the contents of which are hereby incorporated by referenceherein in its entirety.

BACKGROUND

Given the line-of-sight (LoS) characteristics of future generationwireless networks (e.g., Fifth Generation (5G) networks), determiningwith precision prospective service coverage for individual potentialuser sites within a structure (e.g., multi-dwelling units, multi-tenantbusiness complexes, etc.) presents technological challenges.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an exemplary environment in which anexemplary embodiment of a service qualification may be implemented;

FIG. 2 a diagram illustrating exemplary components of a device that maycorrespond to one or more of the devices illustrated herein;

FIG. 3A is a diagram illustrating an exemplary image captured at anexemplary radio site;

FIG. 3B is a diagram illustrating exemplary image data for use in anexemplary embodiment of a service qualification described herein;

FIG. 3C is a diagram illustrating exemplary building information for usein the exemplary embodiment of the service qualification; and

FIGS. 4A-4C are flow diagrams illustrating an exemplary process of anexemplary embodiment of the service qualification.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The following detailed description refers to the accompanying drawings.The same reference numbers in different drawings may identify the sameor similar elements. Also, the following detailed description does notlimit the invention.

Wireless access networks may operate within a frequency spectrum, suchas between about 1 to about 100 Gigahertz. Wireless devices in suchnetworks may be deployed using LoS configurations between wirelessstations. Although the future generation wireless network is designedwith improvements in mind for various network parameters, such astraffic capacity, latency, data throughput, etc., these prospectivebenefits may not be realized for every user location due to variousfactors. For example, certain building configurations and/orenvironmental settings may reduce expected network metrics at differentplacements and distances of users' receiving equipment from a futuregeneration transmission site. Thus, a need exists for a technologicalsolution for accurately and efficiently identifying a coverage area fora future generation antenna transmitter/receiver or radio unit, anddifferentiating between qualified/non-qualified user sites within thetransmitter/receiver's service area or coverage area.

According to an exemplary embodiment, a coverage locator system isdescribed that assesses the extent of future generation wireless accessfor a given location. For example, a future or a next generationwireless access may be a 5G technology. As used herein, the term 5G isreferring to an advanced or next generation wireless network and is notintended to limit the disclosed embodiments to any specific standard orevolution of advanced wireless networks. According to various exemplaryimplementations, the location may be a user's residence (e.g., anapartment complex, a condominium, etc.), a place of business (e.g., anoffice building, a shopping center, etc.), a public facility (e.g., amunicipal building, a school campus, etc.), or other type of building.Using this technological solution, installation of 5G equipment may belimited to those user sites that “qualify” for 5G access in advance ofobtaining and setting up customer premises equipment (CPE) for receiving5G wireless service. In this manner, resources and equipment are notexpended for unnecessary installs at user sites for which it isdetermined that 5G service would not be accessible.

According to an exemplary embodiment, the service qualification processor coverage locator system calculates an expected signal strength lossalong a path from the radio site to a potential user site located in abuilding. According to an exemplary implementation, LoS informationobtained by a smart coverage locator system at the radio site is used toestimate path loss to individual potential user sites at the location.For example, LoS data may be acquired using computer vision generated,for example, from data acquired by a camera device and/or a ray tracingtool located with an antenna transmitter (e.g., pole-mounted radio unit)at a radio site, and processed by an analytics engine, etc., locallyand/or remotely at a network device. According to an exemplaryembodiment, the analytics engine, for example, may be instructed to usesignal strength loss modeling with respect to one or more obstructionsidentified in images of a path from a radio site to potential user sitelocations that may be pinpointed using the LoS data and/or otherinformation. The service qualification system may, for example,calculate and model a signal loss value based on nominal loss valuesassigned to particular types of obstructions (e.g., foliage, signage,structures, etc.). As used here, “LoS” may include a clear, unobstructedLoS, and/or a would-be LoS path that is at least partially obstructed byone or more objects.

According to an exemplary implementation, the signal strength values maybe calculated for locations at the building exterior which areassociated with potential user sites. The coverage locator system mayfurther determine signal strength values based on penetration lossthrough the building exterior (e.g., walls, glass windows/doors,low-emissivity (low-E) glass, etc.) to indoor potential user sites.

According to an exemplary embodiment, the qualification service includescalculating a 5G received signal power based on the 5G path loss.According to an exemplary embodiment, the 5G received signal power maybe compared to a threshold value to determine whether a potential usersite qualifies for 5G equipment installation for 5G access. When the 5Greceived signal power meets the threshold value, the qualificationservice may determine that the location is a candidate 5G user site.However, when the 5G received signal power does not satisfy thethreshold value, the qualification service may determine that thelocation is not a candidate 5G user site. According to an exemplaryembodiment, a service is described that initiates and/or directsinstallation of the 5G receiving equipment at candidate 5G candidateuser sites per user requests.

According to an exemplary embodiment, the coverage locator system usesLoS data to identify a building, user information associated with thebuilding, and/or public or other records regarding thelayout/orientation of the building to substantially determine individualpotential user sites within the building. According to an exemplaryimplementation, the coverage locator system uses a machine learningalgorithm that may modify estimated coverage values based on successfulinstallations at existing user sites within the same building. Accordingto an exemplary implementation, the coverage locator system may makeseasonal adjustments to estimated coverage values where one or more LoSparameters may be affected by climate. For example, it may be determinedthat one or more of the identified obstructions includes foliage that issubject to seasonal changes.

FIG. 1 is a diagram illustrating an exemplary environment 100 in whichan exemplary embodiment of a service qualification or coverage locatormay be implemented. As illustrated, environment 100 includes customerpremises equipment (CPE) 110, a radio site 120, a radio unit 130,customer premises 140-1 to 140-N, a network 150, and a network device160. Environment 100 may also include CPE management element system 170,client device 180, and operator 190. The number and arrangement ofdevices in environment 100 are exemplary. According to otherembodiments, environment 100 may include additional devices and/ordifferently arranged devices, than those illustrated in FIG. 1.

A network device may be implemented according to a centralized computingarchitecture, a distributed computing architecture, or a cloud computingarchitecture (e.g., an elastic cloud, a private cloud, a public cloud,etc.). Additionally, a network device may be implemented according toone or multiple network architectures (e.g., a client device, a serverdevice, a peer device, a proxy device, and/or a cloud device). Forexample, network device 160 or CPE element management system 170 may beimplemented according to various computing architectures and/or one ormultiple network architectures.

CPE 110 includes one or more network devices of a wireless accessnetwork which have computational and wireless communicativecapabilities. For example, CPE 110 may be implemented as a modem capableof receiving radio signals, a router, a transceiver and/or other networkequipment, which together may function as a wireless access point to theradio network.

Radio site 120 may include a network device of a wireless access networkthat has wireless capabilities or wireless capabilities andcomputational capabilities to be installed or already installed. Forexample, radio site 120 may include a radio unit 130 that includes anantenna transmitter 134 that may be implemented as a radio remote unit(RRU). According to another example, antenna transmitter 134 may beimplemented as an integrated Radio Unit (RU) and a baseband unit (BBU).According to an exemplary implementation, the network device operatesaccording to a future generation wireless technology. For example, RRUand/or RU/BBU may be implemented as a wireless device of a 5G wirelessaccess network or a 5G wireless fronthaul network. The network devicemay wirelessly communicate within a frequency band between about 1 GHzto about 100 GHz. For example, the network device may transmit in aboutthe 28 GHz spectrum, the 39 GHz spectrum, or some other spectrum belowabout 100 GHz. In one embodiment, antenna transmitter 134 may have atransmission angle of a range of angles, such as about 30-180 degrees,e.g., about 130 degrees or any other angle.

According to an exemplary embodiment, radio unit 130 includes a camera136 that records digital video and/or images in a vicinity of radio site120. Camera 136 may be mounted, for example, on a pole, on a tower,etc., with antenna transmitter 134. Camera 136 may be moveably attachedto the pole, i.e., having panning and/or tilting capabilities. Fixed ormovable, camera 136 may have a viewing angle that is equal to or greaterthan the transmission angle of antenna transmitter 134. For example,camera 136 may have a viewing angle between about 30 and 180 degrees,e.g., about 130 degrees or any other angle. Camera 136 may capture,store, and/or transmit image data that includes, for example, timestampinformation, directional information, location information, or othermetadata etc., using IP or other communication protocols. In anexemplary implementation, camera 136 may use a ray tracing tool (notillustrated) that is capable of tracing the movements of camera 136. Inone embodiment, the ray tracing tool may be used in detecting reflectivesurfaces, glass (e.g., windows, doors, etc.) located on buildingexteriors. Data obtained using the ray tracing tool may be stored withimage data captured by camera 136.

According to an exemplary embodiment, radio unit 130 includes ananalytics engine 138 including logic that provides a qualificationservice or coverage locator system, as described herein. According to anexemplary implementation, analytics engine 138 stores or has access tostorage (e.g., a remote storage device) containing informationpertaining to the qualification service and the coverage locator system.For example, analytics engine 138 may store or have access to adatabase, as described herein. The information may include datapertaining to locations (e.g., latitude and longitude values, physicaladdresses, etc.) of radio sites, characteristic information pertainingto radio sites, information obtained from antenna transmitter 134,camera 136, and/or other devices, information obtained from athird-party service, threshold values, values pertaining to path loss,and loss values to calculate path losses. According to an exemplaryimplementation, analytics engine 138 uses data from third party sourcespertaining to building locations, orientations, floorplans, occupancy,etc.

Customer premises 140-1 to 140-N (collectively, customer premises 140)may be any type of structure that may be occupied by a user. Forexample, customer premises 140 may be a single family home, a multi-unitdwelling, such as a townhouse, an apartment, a condo, or other type ofmulti-occupant residence. Customer premises 140 may be a commercialcenter, a public venue, an industrial complex, or other type ofinfrastructure (e.g., a medical facility, an educational campus, agovernmental building, a military installation, an office park, etc.).

Network device 160 includes a device that has communication andcomputational capabilities. For example, network device 160 may beimplemented as a computer or a computer and a mass storage device. Byway of further example, network device 160 may include a Web server oran Internet Protocol (IP) server. Network device 160 may reside in anetwork (not illustrated), such as, for example, a private network, apublic network (e.g., the Internet, the World Wide Web, etc.), a widearea network (WAN), a metropolitan area network (MAN), a serviceprovider network, an IP Multimedia Subsystem (IMS) network, a RichCommunication Service (RCS) network, a cloud network, or other type ofnetwork that may be external to the wireless access network and/or acore network to which radio unit 130 belongs or is communicativelycoupled. According to an exemplary implementation, network device 160may be accessed via network 150.

According to an exemplary embodiment, analytics engine 138 and/ornetwork device 160 may calculate a path loss for 5G service based onsite characteristics data obtained by/retrieved from analytics engine138 at radio site 120. According to an exemplary implementation, pathloss values for 5G service are based on LoS data obtained via camera136, particularly automatically-identified building indicia, buildingexterior glass (e.g., non-low-E and/or low-E), obstructing objects(e.g., signage, foliage, etc.), existing user sites in the buildingaccessing 5G service, etc. According to an exemplary embodiment,analytics engine 138 and/or network device 130 calculates estimated 5Greceived signal strength values based on the path loss values forindividual potential user sites for customer premises 140.

According to an exemplary embodiment, analytics engine 138 and/ornetwork device 160 includes a machine learning algorithm. The machinelearning algorithm may analyze and modify various values stored in theinformation, as described herein. For example, values associated withshapes and colors of objects may be used in digital image processing toidentify and differentiate one type of object (e.g., foliage) fromanother type of object (e.g., structure). This data may be grouped in adatabase of like image models and used to categorize an object's impacton LoS signal strength (e.g., attenuation, deflection, etc.). Over time,the characteristic values may be adjusted based on the acquired data.

Network 150 includes one or multiple networks of one or multiple types.For example, network 150 may be implemented to include a terrestrialnetwork, a content delivery network, a wireless network, a wirednetwork, an optical network, a radio access network, a core network, apacket network, an Internet Protocol (IP) network, the Internet, theWorld Wide Web, a private network, a public network, a televisiondistribution network, a streaming network, a mobile network, and/orother type of network that provides access to radio site 120. In oneexemplary embodiment, network 150 may be a backhaul network.

CPE management system 170 includes a device that has computational andwireless communication capabilities. CPE management system 170 may beimplemented as a server device capable of communicating with CPE 110.CPE management system 170 may send instruction messages to CPE 110and/or receive data from CPE 110.

Client 180 includes a device that has computational and wirelesscommunication capabilities. Client 180 may be implemented as a mobiledevice or a portable device and may be operated by operator 190 (e.g.,service provider personnel). For example, client 180 may be implementedas a specialized device, a smartphone, a personal digital assistant, atablet, a laptop, a netbook, a phablet, a wearable device, or some othertype of wireless computational device. According to an exemplaryembodiment, client 180 is configured to communicate with CPE 110 and/oranalytics engine 138. According to an exemplary implementation, client180 may be a wireless device that has 4G, LTE, or LTE-A wirelesscapabilities. Client device 180 may also include other components thatmay be used to support the qualification service or the coveragelocator, as described herein.

FIG. 2 is a diagram illustrating exemplary components of a device 200that may correspond to one or more of the devices described herein. Forexample, device 200 may correspond to components of CPE 110, radio site120, radio unit 130, analytics engine 138, network device 160, CPEelement management system 170 and/or client 180. As illustrated in FIG.2, device 200 includes a bus 205, a processor 210, a memory/storage 215that stores software 220, a communication interface 225, an input 230,and an output 235. According to other embodiments, device 200 mayinclude fewer components, additional components, different components,and/or a different arrangement of components than those illustrated inFIG. 2 and described herein.

Bus 205 includes a path that permits communication among the componentsof device 200. For example, bus 205 may include a system bus, an addressbus, a data bus, and/or a control bus. Bus 205 may also include busdrivers, bus arbiters, bus interfaces, clocks, and so forth.

Processor 210 includes one or multiple processors, microprocessors, dataprocessors, co-processors, application specific integrated circuits(ASICs), controllers, programmable logic devices, chipsets,field-programmable gate arrays (FPGAs), application specificinstruction-set processors (ASIPs), system-on-chips (SoCs), centralprocessing units (CPUs) (e.g., one or multiple cores), microcontrollers,and/or some other type of component that interprets and/or executesinstructions and/or data. Processor 210 may be implemented as hardware(e.g., a microprocessor, etc.), a combination of hardware and software(e.g., a SoC, an ASIC, etc.), may include one or multiple memories(e.g., cache, etc.), etc.

Processor 210 may control the overall operation or a portion ofoperation(s) performed by device 200. Processor 210 may perform one ormultiple operations based on an operating system and/or variousapplications or computer programs (e.g., software 220). Processor 210may access instructions from memory/storage 215, from other componentsof device 200, and/or from a source external to device 200 (e.g., anetwork, another device, etc.). Processor 210 may perform an operationand/or a process based on various techniques including, for example,multithreading, parallel processing, pipelining, interleaving, etc.

Memory/storage 215 includes one or multiple memories and/or one ormultiple other types of storage mediums. For example, memory/storage 215may include one or multiple types of memories, such as, random accessmemory (RAM), dynamic random access memory (DRAM), cache, read onlymemory (ROM), a programmable read only memory (PROM), a static randomaccess memory (SRAM), a single in-line memory module (SIMM), a dualin-line memory module (DIMM), a flash memory, and/or some other type ofmemory. Memory/storage 215 may include a hard disk (e.g., a magneticdisk, an optical disk, a magneto-optic disk, a solid state disk, etc.)and a corresponding drive. Memory/storage 215 may include a hard disk(e.g., a magnetic disk, an optical disk, a magneto-optic disk, a solidstate disk, etc.), a Micro-Electromechanical System (MEMS)-based storagemedium, and/or a nanotechnology-based storage medium. Memory/storage 215may include drives for reading from and writing to the storage medium.

Memory/storage 215 may be external to and/or removable from device 400,such as, for example, a Universal Serial Bus (USB) memory stick, adongle, a hard disk, mass storage, off-line storage, or some other typeof storing medium (e.g., a compact disk (CD), a digital versatile disk(DVD), a Blu-Ray disk (BD), etc.). Memory/storage 215 may store data,software, and/or instructions related to the operation of device 200.

Software 220 includes an application or a program that provides afunction and/or a process. As an example, with reference to server 130,software 220 may include an application that, when executed by processor410, provides the functions of the qualification, as described herein.Also, network device 160 may include an application that, when executedby processor 210, provides the functions of the qualification service,as described herein. Software 220 may also include firmware, middleware,microcode, hardware description language (HDL), and/or other form ofinstruction.

Communication interface 225 permits device 200 to communicate with otherdevices, networks, systems, and/or the like. Communication interface 225includes one or multiple wireless interfaces. Communication interface225 may include one or multiple wired interfaces. For example,communication interface 225 may include one or multiple transmitters andreceivers, or transceivers. Communication interface 225 may operateaccording to a protocol stack and a communication standard.Communication interface 225 may include an antenna. Communicationinterface 225 may include various processing logic or circuitry (e.g.,multiplexing/de-multiplexing, filtering, amplifying, converting, errorcorrection, etc.).

Input 230 permits an input into device 200. For example, input 230 mayinclude a keyboard, a mouse, a display, a touchscreen, a touchlessscreen, a button, a switch, an input port, speech recognition logic,and/or some other type of visual, auditory, tactile, etc., inputcomponent. Output 235 permits an output from device 200. For example,output 235 may include a speaker, a display, a touchscreen, a touchlessscreen, a light, an output port, and/or some other type of visual,auditory, tactile, etc., output component.

Device 200 may perform a process and/or a function, as described herein,in response to processor 210 executing software 220 stored bymemory/storage 215. By way of example, instructions may be read intomemory/storage 215 from another memory/storage 215 (not shown) or readfrom another device (not shown) via communication interface 225. Theinstructions stored by memory/storage 215 cause processor 210 to performa process described herein. Alternatively, for example, according toother implementations, device 200 performs a process described hereinbased on the execution of hardware (processor 210, etc.).

FIGS. 3A-3C are diagrams illustrating an exemplary wireless servicequalification process and coverage locator system. According to variousembodiments, an operation or a step described in the process may beperformed by analytics engine 138, network device 160, or a combinationof network device 160 and analytics engine 138. Also, a communicativeconnection between network devices (e.g., CPE 110 and analytics engine138) and/or between network device 160 and network devices (e.g., CPE110, analytics engine 138) may be indirect. For example, an indirectcommunication connection may involve an intermediary device and/or anintermediary network not illustrated in FIGS. 3A-3C.

Referring to FIG. 3A, an image 300 may be captured by camera 136 ofradio unit 130 mounted at a pole, for example, on a street corner of anintersection. Image 300 may correspond to a photo and/or a segment of avideo recording that is automatically obtained (e.g., at predeterminedtimes, regular and/or irregular intervals, etc.) and/or in response to atrigger event (e.g., an installation of radio unit 130, a customerrequest for service, a change of season, a customer complaint, adetected change in signal coverage and/or data traffic, etc.). In oneembodiment, camera 136 may be configured to provide image 300 as avisual representation that substantially corresponds to a radio coveragearea at radio site 120. For example, when a position of camera 136 isfixed, camera 136 may be provided with a lens having a viewing anglethat is coextensive with or greater than a transmission angle of antennatransmitter 134. Alternatively, when camera 136 has a lens with aviewing angle that is less than a transmission angle of antennatransmitter 134, camera 136 may be provided with panning capabilitiesthat expand the viewing angle to be coextensive with or greater than theassociated transmission angle.

In one embodiment, camera 136 may be integrated with antenna transmitter134 and/or analytics engine 138. Alternatively, camera 136 may be aseparate device that is mounted together with antenna transmitter 134.As an alternative to being mounted at radio site 120, camera 136 may bea mobile device that is temporarily located at radio site 120. Forexample, camera 136 may be associated with a drone device that isautomatically re-located to/from radio site as needed and/or directed.

In one embodiment, images recorded by camera 136 include metadata (e.g.,a timestamp, location information, orientation information, etc.)obtained by camera 136, and/or other information generated by otherdevices, such as a ray tracing tool associated with radio unit 130.Metadata from camera 136 may be associated with information identifyingradio unit 130 and/or geographic information for the location. Camera136 may store recorded information in any number of formats forretrieval and/or for sending, for example, to analytics engine 138. Insome embodiments, a live video stream may be provided to network device160 via network 150. In one embodiment, camera 136 is GPS-enabled, andthe metadata includes location information (e.g., latitude and longitudecoordinates, altitude, physical address, etc.)

Analytics engine 138 associated with radio unit 130 obtains the imagedata associated with image 300 from camera 136. In one embodiment,analytics engine 138 performs image processing of image 300 to identifyobjects captured in the images, and their relative locations, forexample, with respect to antenna transmitter 134. Image data 310 isillustrative of an augmented reality representation generated from imageprocessing that may be performed on image 300. In one embodiment, someor all of the image processing may be performed by camera 136 or anotherdevice.

Analytics engine 138 identifies an exterior of a building 330 in imagedata 310. For example, analytics engine 138 may use object recognitionlogic to determine that building 330 is a user-occupied structure, anddirectional information (e.g., GPS) may be used to identify one or morefaces of the building exterior (e.g., based on cross street informationassociated with radio site 120, which is retrieved from a database). Fora building that has a single street address, analytics engine 138 maysearch for other indicia that further identify the building. Forexample, analytics engine 138 may identify a main entrance to building330 and locate, on a marquee, canopy, overhang, etc., a building number340 or other identifying mark. Referring to FIG. 3B, according to thisexample, the image area at building number 340 indicates that building300 is building “#3.” Other reference information may be identified, forexample, “East Entrance,” etc., that may be used to determine anorientation of building 330. Other indicia (e.g., signage, etc.) may beused to determine that building 330 is a multi-unit or multi-suitebuilding, which may be occupied by more multiple users potentially usingmultiple 5G user sites.

Using some or all of the indicia identified on the faces of thebuilding, analytics engine 138 may retrieve, from network device 160,for example, geographic information for building 330, such as a physicaladdress, and/or survey information including distance informationindicating distances of the faces of building 330 to radio unit 130. Inone embodiment, the survey information may include beam referencesignals received power (RSRP) values for locations on the faces ofbuilding 330. In one embodiment, the RSRP values may be based oncalculations from transmission parameters for antenna transmitter 134and/or measurements performed using measurement devices on location. TheRSRP values may account for distance-related air loss for signals alongthe paths from antenna transmitter 134 to locations on a face ofbuilding 330, while not accounting for obstructions in the LoS betweencamera 136 and building 330.

In one embodiment, analytics engine 138 uses the information identifyingbuilding 330 to search records and public information regarding building330. In one embodiment, analytics engine 138 may retrieve from networkdevice 160, for example, information regarding plats, propertydescriptions, architectural drawings, floorplans, etc. from public orprivate records, advertisement materials, building plans, Freedom ofInformation Act (“FOIA”) documents, etc. Referring to FIG. 3C, afloorplan diagram 320 for building 330 may be retrieved by analyticsengine 138 from a database associated with network device 160 inresponse to a query identifying the street address and/or commercialname of building 330.

Floorplan diagram 320 illustrates the interior layout for a particularfloor in building 330, in which the relative locations of individualapartments, hallways, common areas, and/or outer walls may be to scale.In one embodiment, analytics engine 138 may correlate floorplan diagram320 to building 330. That is, the building exterior shown in image data310 may be mapped to corresponding apartment locations depicted infloorplan diagram 320. For example, an apartment 350 may be identifiedin image data 310 and in floorplan diagram 320. In some instances,analytics engine 138 may determine that no public information regardingbuilding 300 is available. For some buildings 330, analytics engine 138may retrieve more detailed public information. For other buildings 330,analytics engine 138 may be unable to determine an orientation ofbuilding 330 from the publicly available information.

In one embodiment, analytics engine 138 may determine that LoS fromradio unit 130 to apartment 350, for example, exists. Analytics engine138 may analyze image data from image data 310 to determine the LoS or apath is obstructed or not between antenna transmitter 134 and apartment350. Based on the image data, analytics engine 138 may identify one ormore objects along the LoS. For example, analytics engine 138 mayidentify a tree image 360 that at least partially obstructs the would-beLoS. In one embodiment, analytics engine 138 may determine a type oftree for tree image 360. In another embodiment, analytics engine 138 mayassign a qualitative value (e.g., low, medium, high, etc.) or aquantitative value (e.g., a percentage, a number between one and ten,etc.) to an extent of the obstruction. In some embodiments, whenanalytics engine 138 cannot determine from image data 310 that treeimage 360 is in fact a tree, analytics engine 138 may classify theobstruction more broadly, such as “foliage” or the like. Otherobstructions in the would-be LoS may be classified by analytics engine138 from image data 310, as signage, structures, or other types ofobstructions.

Analytics engine 138 analyzes image data 310 to determine a type ofmaterial in the building exterior of apartment 350 that the radiosignals must pass through to an interior of apartment 350. For example,analytics engine may determine that the LoS does not terminate at glassat the building exterior, or that the LoS does terminate at a glass dooror glass window 370 in the building exterior. In one embodiment,analytics engine 138 may determine that an object is at least partiallycovering glass window, e.g., shutters, an awning, etc. In anotherembodiment, analytics engine 138 may determine a type of the glass, forexample, low-emissivity (low-E), or non-low-E. In one embodiment,analytics uses data from a ray tracing tool located at radio unit 130.

Analytics engine 138 analyzes image data 310 to determine whether anysuccessful installation of radio receiving equipment (CPE) at anotherapartment in building 330. Analytics engine 138 may request and receiveinformation from network device 160 regarding current wireless serviceuser locations proximate to apartment 350. For example, analytics engine138 may determine that an apartment 380 is a user location currentlyreceiving wireless service. Analytics engine 138 may request and receiveinformation regarding signal strength associated with the user locationat apartment 380. For example, signal information may include a ReceivedSignal Strength Indicator (RSSI), and/or a Reference Signal ReceivedQuality (RSRQ), such as signal-to-noise ratio (SNR),signal-to-interference-plus-noise ratio (SINR), or other channelcondition value measured by a CPE.

FIGS. 4A-4C are flow diagrams illustrating an exemplary process 400pertaining to a smart 5G coverage locator system. Process 400 isdirected to a process previously described above with respect to FIGS.3A-3C, as well as elsewhere in this description. According to anexemplary embodiment, analytics engine 138 and network device 160perform steps of process 400. For example, processor 210 executessoftware 220 to perform the steps illustrated in FIGS. 4A-4C anddescribed herein.

Referring to FIG. 4A, block 405, a coverage locator system recordsimages (e.g., photo, video) from a wireless radio site. The recordedimages may be stored with associated metadata, for example, withtemporal information that may demonstrate, for example, seasonal effectsand/or other changes in the environmental setting of the radio site thatmay or may not affect LoS from the radio antenna transmitter.

In block 408, analytics engine 138 analyzes the image data to determinethat an image of a building corresponds to a multi-occupant building.For example, analytics engine 138 may recognize text or other indicia,e.g., visible signage, that describes a building's location and/or thenature of its use, and determine that the building is a multi-unitdwelling, a multi-suite office building, etc. According to someexemplary implementations, analytics engine 138 may, at block 410,search a records database using the building identification information,and retrieve public or other information that describes, in varyingdetail, physical aspects of the building (e.g., entrances, evacuationroutes, passageways, interior layout, etc.).

In block 415, analytics engine 138 may analyze the image data usinginformation regarding the interior layout of the building to identify apotential user site (e.g., apartment) in the building. In block 420,analytics engine 138 may retrieve predicted beam RSRP values from adatabase for a path (of a known distance) from the radio unit to theexterior of the building corresponding to the potential user site withinthe building.

In block 425, analytics engine 138 analyzes the image data to determinewhether a clear LoS can be established between the radio unit and theexterior of the potential user site. Analytics engine 138 may identifyany object that may obstruct the radio signal and thus contribute topath loss and diminished signal strength.

When a clear, unobstructed LoS can be established along a path to thepotential user site (block 425—YES), then process 400 may proceed toblock 455 in FIG. 4B (block 430). Alternatively, when it is determinedthat the LoS is obstructed by one or more objects (block 425—NO), thenanalytics engine 138 may determine whether the obstruction visible fromthe image data can be classified as foliage (block 435). For example,analytics engine 138 may run object recognition logic to classify eachobstruction.

When foliage (e.g., a tree) is detected (block 435—YES), analyticsengine 138 may subtract a path loss value, from the predicted RSRPvalue, which value corresponds to the type and/or dimensions of thefoliage detected (block 438), and may be retrieved from a database ofassigned values, then process 400 may proceed to block 440 in FIG. 4B.Alternatively, when it is determined that the obstruction is not foliage(block 435—NO), then process 400 may continue to block 440 in FIG. 4Bwithout modification to the predicted RSRP value.

Referring to FIG. 4B, in block 440, analytics engine 138 may determinewhether the obstruction visible from the image data can be classified asa structure. For example, analytics engine 138 may detect signage or abuilding that obstructs the LoS (block 440—YES), in which case,analytics engine 138 may subtract a path loss value, from the predictedRSRP value, which value corresponds to the type and/or dimensions of thestructure detected (block 442), and may be retrieved from a database ofassigned values. In one embodiment, when the obstruction cannot beclassified as a structure (block 440—NO), no modification of thepredicted RSRP value is made and process 400 continues to block 445.

In some embodiments, when analytics engine 138 detects a structure thatobstructs the LoS (block 440—YES), process 400 may include scheduling asubsequent capturing of an image at the radio site (block 405) todetermine whether the structure can still be detected. In oneembodiment, analytics engine 138 may capture the subsequent image at adifferent time of day than the time of day associated with the initialimage (e.g., non-business hours versus business hours), and/or on adifferent day of the week than the day of the week associated with theinitial image (e.g., weekend versus weekday). Process 400 may includeusing the subsequent image(s) to determine whether theoriginally-detected object is a permanent structure (e.g., sign, etc.)or a transient (e.g., a motor vehicle, construction equipment, etc.).Analytics engine 138 may obtain multiple subsequent images to determinea likelihood that structure is a permanent one. When analytics engine138 determines that the structure was a transient and no longer in theLoS, the predicted RSRP value may be calculated, for example, byomitting the action described in block 442. When analytics engine 138determines that the structure is permanent, process 400 continues toblock 445.

In block 445, analytics engine 138 may analyze the image data, includingdata from a ray tracing tool, to determine whether a glass window and/ora glass door is visible on the building exterior in an area of thepotential user site. When no glass is detected (block 445—NO), analyticsengine 138 may subtract a penetration loss value, from the predictedRSRP value, which value corresponds to the type of exterior wall of thebuilding (block 450), and may be retrieved from a database of assignedvalues. Process 400 may continue to block 470.

When glass is detected in the building exterior (block 445—YES),analytics engine 138 may determine a type of glass, for example, whetherthe glass is low-E glass (block 455). When analytics engine 138determines that the glass is low-E glass (block 455—YES), analyticsengine 138 may subtract a penetration value, from the predicted RSRPvalue, which value corresponds to low-E glass (block 465), and may beretrieved from a database of assigned values. Alternatively, whenanalytics engine 138 determines that the glass is not low-E glass (block455—NO), analytics engine 138 may subtract a penetration value, from thepredicted RSRP value, which value corresponds to non-low-E glass (block460), and may be retrieved from a database of assigned values.

In block 470, analytics engine 138 may determine whether other usersites with successful wireless equipment installation in the buildingare proximate to the potential user site. If no other user sites arelocated (block 470—NO), then process 400 may continue to block 475 (FIG.4C). When other user sites are proximate to the potential user site(block 470—YES), analytics engine 138 may modify the predicted RSRPvalue based on signal information available for the other user sites,for example SNR values.

Referring to FIG. 4C, in block 475, analytics engine 138 may compare thepredicted RSRP value with a 5G RSRP threshold value. In block 480,analytics engine 138 determines whether the predicted RSRP valuesatisfies the threshold value. For example, analytics engine 138 maydetermine whether the calculated 5G received signal power valuesatisfies the 5G received signal power threshold value based on thecomparison.

When it is determined that the predicted 5G RSRP does not satisfy thethreshold value (block 480—NO), then analytics engine 138 determinesthat the potential user site has inadequate 5G coverage and is thereforeineligible for 5G service installation (block 485). When it isdetermined that the predicted 5G RSRP does satisfy the threshold value(block 480—YES), then the potential user site has adequate 5G coverageand is therefore a candidate for 5G service installation (block 490). Inblock 495, analytics engine 138 may automatically initiate 5G serviceequipment installation for the potential user site, for example, which aservice install request from the user is pending. For example, analyticsengine 138 may generate a notification indicating that the potentialuser site is qualified for 5G service. The notification may be sent to auser requesting service and/or to the service provider. Additionally oralternatively, analytics engine 138 may automatically generate an orderfor distribution and/or set-up of 5G receiving equipment at thecandidate (i.e., qualified) user site.

Although FIGS. 4A-4C illustrate an exemplary process 400 of the servicequalification, according to other embodiments, process 500 may includeadditional operations, fewer operations, and/or different operationsthan those illustrated in FIGS. 4A-4C and described herein. Also, inprocess 400, a service provider may use captured image data from a radiosite to address service issues form existing 5G users in the coveragearea. For example, the image data may reveal that storm damage hascreated one or more obstructions in the LoS to one or more existing usersites.

The foregoing description of embodiments provides illustration, but isnot intended to be exhaustive or to limit the embodiments to the preciseform disclosed. In the preceding description, various embodiments havebeen described with reference to the accompanying drawings. However,various modifications and changes may be made thereto, and additionalembodiments may be implemented, without departing from the broader scopeof the invention as set forth in the claims that follow. The descriptionand drawings are accordingly to be regarded as illustrative rather thanrestrictive. In this way, the service qualification and the coveragelocator system described herein may be implemented to calculate biasedvalues (e.g., path loss, received signal power, etc.) based on othercurrent or legacy wireless technologies and/or a frequency bandsmeasured.

In addition, while series of blocks have been described with regard tothe processes illustrated in FIGS. 4A-4C, the order of the blocks may bemodified according to other embodiments. Further, non-dependent blocksmay be performed in parallel. Additionally, other processes described inthis description may be modified and/or non-dependent operations may beperformed in parallel.

The embodiments described herein may be implemented in many differentforms of software executed by hardware. For example, a process or afunction may be implemented as “logic” or as a “component.” The logic orthe component may include, for example, hardware (e.g., processor 210,etc.), or a combination of hardware and software (e.g., software 220).The embodiments have been described without reference to the specificsoftware code since the software code can be designed to implement theembodiments based on the description herein and commercially availablesoftware design environments/languages.

As set forth in this description and illustrated by the drawings,reference is made to “an exemplary embodiment,” “an embodiment,”“embodiments,” etc., which may include a particular feature, structureor characteristic in connection with an embodiment(s). However, the useof the phrase or term “an embodiment,” “embodiments,” etc., in variousplaces in the specification does not necessarily refer to allembodiments described, nor does it necessarily refer to the sameembodiment, nor are separate or alternative embodiments necessarilymutually exclusive of other embodiment(s). The same applies to the term“implementation,” “implementations,” etc.

The terms “a,” “an,” and “the” are intended to be interpreted to includeone or more items. Further, the phrase “based on” is intended to beinterpreted as “based, at least in part, on,” unless explicitly statedotherwise. The term “and/or” is intended to be interpreted to includeany and all combinations of one or more of the associated items.

The word “exemplary” is used herein to mean “serving as an example.” Anyembodiment or implementation described as “exemplary” is not necessarilyto be construed as preferred or advantageous over other embodiments orimplementations.

Use of ordinal terms such as “first,” “second,” “third,” etc., in theclaims to modify a claim element does not by itself connote anypriority, precedence, or order of one claim element over another, thetemporal order in which acts of a method are performed, the temporalorder in which instructions executed by a device are performed, etc.,but are used merely as labels to distinguish one claim element having acertain name from another element having a same name (but for use of theordinal term) to distinguish the claim elements.

Additionally, embodiments described herein may be implemented as anon-transitory storage medium that stores data and/or information, suchas instructions, program code, data structures, program modules, anapplication, etc. The program code, instructions, application, etc., isreadable and executable by a processor (e.g., processor 210) of acomputational device. A non-transitory storage medium includes one ormore of the storage mediums described in relation to memory/storage 215.

To the extent the aforementioned embodiments collect, store or employpersonal information of individuals, it should be understood that suchinformation shall be collected, stored, and used in accordance with allapplicable laws concerning protection of personal information.Additionally, the collection, storage and use of such information may besubject to consent of the individual to such activity, for example,through well known “opt-in” or “opt-out” processes as may be appropriatefor the situation and type of information. Collection, storage, and useof personal information may be in an appropriately secure mannerreflective of the type of information, for example, through variousencryption and anonymization techniques for particularly sensitiveinformation.

No element, act, or instruction described in the present applicationshould be construed as critical or essential to the embodimentsdescribed herein unless explicitly described as such.

What is claimed is:
 1. A method comprising: receiving, by a networkdevice, line-of-site (LoS) information obtained by a camera, wherein theLoS information identifies an LoS from a radio site to a potential radiosignal receiver location; estimating, by the network device, a signalstrength value, for a signal from the radio site, at the potential radiosignal receiver location; determining, by the network device, that theestimated signal strength value exceeds a threshold signal valueassociated with a wireless service; identifying, by the network device,a first type of obstruction in the LoS, wherein the first type ofobstruction comprises foliage having an obstructive profile and anassociated signal loss that vary by a time of year; modifying, by thenetwork device, the estimated signal strength value using a nominalsignal loss value for the first type of obstruction, wherein the nominalsignal loss value is associated with a different time of year than atime of year in which the LoS information is obtained; comparing, by thenetwork device, the modified estimated signal strength value to thethreshold signal value; and determining, by the network device based onresults of the comparing, a qualification of the potential radio signalreceiver location for an equipment installation for the wirelessservice.
 2. The method of claim 1, wherein identifying the first type ofobstruction comprises: using image recognition processing todifferentiate the first type of obstruction from a second type ofobstruction for which an associated signal loss does not vary the timeof year.
 3. The method of claim 1, further comprising: determining avalue for one or more dimensions of the first type of obstruction; anddetermining the nominal signal loss value based on the value for one ormore dimensions.
 4. The method of claim 1, wherein the LoS informationincludes a timestamp, the method further comprising: determining thedifferent time of year based on the timestamp.
 5. The method of claim 1,further comprising: detecting a triggering event at the radio site,wherein the LoS information is obtained by the camera in response to thedetecting.
 6. The method of claim 5, wherein the triggering eventcomprises a customer service order.
 7. The method of claim 1, whereinthe potential radio signal receiver location corresponds to an exteriorof a structure, the method further comprising: subtracting, from themodified estimated signal strength, a signal loss value associated withpenetrating the exterior of the structure; and determining whether toqualify the potential radio signal receiver location for the equipmentinstallation at an interior of the structure.
 8. A network devicecomprising: a communication interface; a memory that storesinstructions; and a processor that executes the instructions to: receiveline-of-site (LoS) information obtained by a camera, wherein the LoSinformation identifies an LoS from a radio site to a potential radiosignal receiver location; estimate a signal strength value, for a signalfrom the radio site, at the potential radio signal receiver location;determine that the estimated signal strength value exceeds a thresholdsignal value associated with a wireless service; identify a first typeof obstruction in the LoS, wherein the first type of obstructioncomprises foliage having an obstructive profile and an associated signalloss that vary by a time of year; modify the estimated signal strengthvalue using a nominal signal loss value for the first type ofobstruction, wherein the nominal signal loss value is associated with adifferent time of year than the time of year in which the LoSinformation is obtained; compare the modified estimated signal strengthvalue to the threshold signal value; and determine, based on results ofthe comparison, a qualification of the potential radio signal receiverlocation for an equipment installation for the wireless service.
 9. Thenetwork device of claim 8, wherein to identify the first type ofobstruction, the processor further executes the instructions to: useimage recognition processing to differentiate the first type ofobstruction from a second type of obstruction for which an associatedsignal loss does not vary by the time of year.
 10. The network device ofclaim 8, wherein the processor further executes the instructions to:determine a value for one or more dimensions of the first type ofobstruction, and determine the nominal signal loss value based on thevalue for one or more dimensions.
 11. The network device of claim 8,wherein the LoS information includes a timestamp, and the processorfurther executes the instructions to: determine the different time ofyear based on the timestamp.
 12. The network device of claim 8, whereinthe processor further executes the instructions to: detect a triggeringevent at the radio site, wherein the LoS information is obtained by thecamera in response to the detection.
 13. The network device of claim 12,wherein the triggering event comprises a customer service report. 14.The network device of claim 8, wherein determining whether to qualifythe potential radio signal receiver location corresponds to an exteriorof a structure, and the processor when determining whether to qualifythe potential radio signal receiver location further executes theinstructions to: subtract, from the modified estimated signal strength,a signal loss value associated with penetrating the exterior of thestructure, and determine whether to qualify the potential radio signalreceiver location for the equipment installation at an interior of thestructure.
 15. A non-transitory, computer-readable storage mediumstoring instructions which, when executed by a processor of a networkdevice, cause the network device to: receive line-of-site (LoS)information obtained by a camera, wherein the LoS information identifiesan LoS from a radio site to a potential radio signal receiver location;estimate a signal strength value, for a signal from the radio site, atthe potential radio signal receiver location; determine that theestimated signal strength value exceeds a threshold signal valueassociated with a wireless service; identify a first type of obstructionin the LoS, wherein the first type of obstruction comprises foliagehaving an obstructive profile and an associated signal loss that vary bya time of year; modify the estimated signal strength value using anominal signal loss value for the first type of obstruction, wherein thenominal signal loss value is associated with a different time of yearthan the time of year in which the LoS information is obtained; comparethe modified estimated signal strength value to the threshold signalvalue; and determine, based on results of the comparison, aqualification of the potential radio signal receiver location for anequipment installation for the wireless service.
 16. The non-transitory,computer-readable storage medium of claim 15, wherein to identify thefirst type of obstruction, the instructions, when executed by theprocessor, further cause the network device to: use image recognitionprocessing to differentiate the first type of obstruction from a secondtype of obstruction for which an associated signal loss does not vary bythe time of year.
 17. The non-transitory, computer-readable storagemedium of claim 15, wherein the instructions, when executed by theprocessor, further cause the network device to: determine a value forone or more dimensions of the first type of obstruction, and determinethe nominal signal loss value based on the value for one or moredimensions.
 18. The non-transitory, computer-readable storage medium ofclaim 15, wherein the LoS information includes a timestamp, and theinstructions, when executed by the processor, further cause the networkdevice to: determine the different time of year based on the timestamp.19. The non-transitory, computer-readable storage medium of claim 15,wherein the instructions, when executed, cause the network device to:detect a triggering event at the radio site, wherein the LoS informationis obtained by the camera in response to the detection.
 20. Thenon-transitory, computer-readable storage medium of claim 15, whereinthe potential radio signal receiver location corresponds to an exteriorof a structure, and the instructions to determine whether to qualify thepotential radio signal receiver location, when executed, cause thenetwork device to: subtract, from the modified estimated signalstrength, a signal loss value associated with penetrating the exteriorof the structure, and determine whether to qualify the potential radiosignal receiver location for the equipment installation at an interiorof the structure.